Probabilistic Reasoning in Dynamic Multiagent Systems

Probabilistic reasoning with multiply sectioned Bayesian networks (MSBNs) has been successfully applied in static domains under the cooperative multiagent paradigm. Probabilistic reasoning in dynamic domains under the same paradigm involves several issues. This paper proposes an approach to address these issues. Intuitively, observation on current state plays a more important role in the reasoning of the current state than remote historic information. Based on the intuition, we model the entire domain for a period of time into an MSBN and then reason about the state of the dynamic domain period by period exactly. In reasoning the state of a suspected entity, we compute and observe an observable Markov boundary of the entity. This makes observation more efficient and relevant. In MSBNs, an observable Markov boundary of a node may span across all Bayesian subnets. We present an algorithm for cooperative computation of an observable Markov boundary of a set of nodes in MSBNs without revealing subnet structures. Preliminary experiments show the approach works well on our simulated multiagent dynamic domains